Senior Scientist in ML Methods for Spatial Biology

About the role

Bristol Myers Squibb is seeking a Senior Scientist to apply modern computational and machine learning methods to spatial biology and related life sciences data. The ideal candidate will have a strong research background, a track record of independent scientific contributions, and experience solving technical problems creatively.

Responsibilities

• Participate in a growing effort to apply advanced computational techniques to the development of novel therapies for neurologic disease, cancer, and hematologic malignancies.
• Act as a local scientific partner and point of connection for experimental and computational collaborators at the Cambridge or Lawrenceville site, enabling effective cross-functional collaboration with a primarily Europe-based team.
• Analyze spatial and other omics data, including spatial transcriptomics, single-cell RNA-seq, ATAC-seq, and related data types.
• Develop, refine, and evaluate computational methods, ML models, and workflows for large biological datasets.
• Formulate translational and discovery questions as computational problems, interpret model outputs in biologically meaningful ways, and generate hypotheses that can guide experimental biology.
• Creatively propose hypotheses and test them rigorously.
• Author scientific reports and present methods, results, and conclusions to a publishable standard.

Requirements

• Bachelor's Degree in a relevant field with 7+ years of academic / industry experience, or Master's Degree with 5+ years of academic / industry experience, or PhD with 2+ years of academic / industry experience.
• Ph.D. in machine learning, bioinformatics, computational biology, or a related technical field.
• Experience applying contemporary computational methods to biological problems.
• Publication record in relevant conferences or journals.
• Experience applying and/or developing ML methods to bioimaging, bioinformatics, single-cell omics, or spatial omics problems.
• Experience with at least one ML framework, such as scikit-learn, PyTorch, or TensorFlow.
• Fluency in written and spoken English.

Preferred Qualifications

• Two or more years of postdoctoral experience in a relevant field.
• Experience analyzing clinical trial-derived data or images to address translational research questions or applying omics data analysis to public datasets for target discovery and prioritization.
• Prior research experience in pharma, biotech, academic, or hospital environments.
• Experience managing multimodal data, including omics, imaging, or time-series signals.
• Experience using cloud-based computing and software engineering tools or frameworks, such as Docker or Git.

Skills

• Strong communication skills.
• Comfort working independently in a distributed environment.
• A proactive approach to building productive scientific relationships across disciplines and locations.
• Demonstrated ability to work independently in a distributed team environment.

Benefits

Bristol Myers Squibb offers a wide variety of competitive benefits, services, and programs designed to support our employees' well-being and financial security. These include:

• Health Coverage: Medical, pharmacy, dental, and vision care.
• Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
• Financial Well-being and Protection: 401(k) plan, short- and long-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.

Pay

The starting compensation range for this role is $128,890 - $156,179 for a full-time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available.

Schedule

This role requires strong communication skills, comfort working independently in a distributed environment, and a proactive approach to cross-functional collaboration. The candidate will work as part of a multidisciplinary team focused on bringing advanced ML/AI approaches to impactful biological questions. They will collaborate with computational and experimental scientists with expertise in machine learning, structural biology, chemistry, cell therapy, and gene therapy.